There has been considerable research into service quality over the last couple of decades. Services, however, as intangible, perishable, and heterogenic transactions are very difficult to quantify and measure, and little success has been reported on a systematic approach in modeling of quality of service transactions (with SERVQUAL and its derivatives as the notable exception). In this chapter, we propose artificial neural networks (ANNs) to monitor quality of service transaction as a dynamic and real-time control and forecasting system. ANNs are widely used in many engineering fields to model and simulate complex systems. The resulting near-perfect models are particularly suited for applications where real-world complexities make it difficult or even impossible to mathematically model and control the system. The proposed approach alleviates restrictions and limitations of applying questionnaire-based static methods, even in cases where there are large number of correlated attributes as well as obscure and unobservable quality characteristics. We illustrate with a case vignette in a healthcare context, thereby demonstrating the suitability of such techniques for healthcare delivery a vital, at times lifesaving service.